Method and apparatus for enhancing a digital image

ABSTRACT

A system and method processes original digital numbers (DNs) provided by a satellite imaging system to produce a set of spectral balanced and contrast enhanced multispectral images. Spectral balancing is achieved based on physical characteristics of sensors of the imaging system as well as compensation for atmospheric effects. The DNs in the multispectral bands may be processed using a relatively small amount of processing resources otherwise required to produce such images. Such images may be processed completely automatically and provide relatively easy visual interpretation. Each image pixel may be, for example, in an 8-bit or 16-bit format, and the image may be displayed and/or printed without applying any additional color correction and/or contrast stretches.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/905,042, filed Dec. 13, 2004.

FIELD OF THE INVENTION

The present invention is directed to enhancing digital images, and, morespecifically, to enhancing color and contrast in a multispectral digitalimage obtained from a remote sensing platform.

BACKGROUND

In a generalized way, a digital image is a set of one or moretwo-dimensional numeric arrays in which each array element, or pixel,represents an apparent brightness measured by an imaging sensor. Thebrightness value at each pixel is often represented by an integer numbercalled a digital number (DN). Digital images are commonly generated byremote imaging systems that collect imagery in the visible and infraredregions of the electromagnetic spectrum. Images collected from suchsystems are used in numerous applications by both commercial andgovernment customers.

When collecting digital imagery, specific bands of electromagneticenergy from the area being imaged are commonly collected at severalimaging sensors. For example, in many imaging systems, several spectralbands of electromagnetic energy are collected at imaging sensors, suchas, a red light band, a green light band, a blue light band, and a nearinfrared band. Imaging systems may also include other spectral bandswithin the visible bands and/or within the middle infrared (a.k.a.,shortwave infrared) bands. An image generated from such a system isreferred to as a multispectral digital image. In such a case, a set ofDN values exist for each line and column position in the multispectraldigital image with one DN value allocated to each spectral band. Each DNrepresents the relative brightness of an image in the associatedspectral band at the associated pixel location in the image. Whengenerating a multispectral digital image, data from the imaging sensorsis collected, processed, and images are produced. Images are commonlyprovided to customers as a multispectral image file containing imageryfrom each of the spectral bands. Each band includes DNs on, for example,an 8-bit or 11-bit radiometric brightness scale representing theradiance collected at the respective sensor for an area of the sceneimaged. Several methods exist for processing data from each band togenerate an image that is useful for the application required by a user.The data is processed in order to provide an image that has accuratecolor and contrast for features within the image.

The DN values generated from a particular imaging sensor have limitedrange that varies from image source to image source according to theassociated bit depth. Commonly used bit depths are 8 bits and 11 bits,resulting in DNs that range from 0 to 255 and 0 to 2047, respectively.Digital images are generally stored in raster files or as raster arraysin computer memory, and since rasters use bit depths that are simplepowers of base 2, image DNs may be stored in rasters having 1, 2, 4, 8,or 16 bits, with 8 bit and 16 bit being most common. It is commonpractice to reserve special DN values to represent non-existent imagedata (e.g., 0, 255, and/or 2047). The corresponding pixels are calledblackfill. Actual image data will then have DNs between 1 and 254 orbetween 1 and 2046.

Digital images, following collection by an imaging system, are commonlyenhanced. Enhancement, in the context of a digital image, is a processwhereby source-image DNs are transformed into new DNs that have addedvalue in terms of their subsequent use. Commonly, the data from eachband of imagery is enhanced based on known sensor and atmosphericcharacteristics in order to provide an adjusted color for each band. Theimage is then contrast stretched, in order to provide enhanced visualcontrast between features within the image. Commonly, when performingthe contrast stretch, the average radiance from each pixel within aparticular band is placed in a histogram, and the distribution of thehistogram is stretched to the full range of DNs available for thepixels. For example, if each band includes DNs on an 8-bit radiometricbrightness scale, this represents a range of DNs between 0 and 255. TheDNs from a scene may then be adjusted to use this full range of possibleDN values, and/or the DNs from a scene may be adjusted to obtain adistribution of DNs that is centered about the mid-point of possible DNvalues. Generally speaking, the visual quality of the contrast stretchachieved using normal contrast stretch algorithms is highly dependent onscene content. Many contrast stretch algorithms change the color contentof the imagery resulting in questionable color content in the scene. Forexample, if the distribution of DNs is not centered within the range ofpossible DN values, such a contrast stretch can skew the DNs resultingin a color offset and, in an image containing structures, a house mayappear as being the wrong color. In addition it is often difficult todecide what stretch to apply to a given image. A user often balances thetrade-offs between acceptable contrast and acceptable color balance whenchoosing a Commercial Off The Shelf (COTS) stretch to apply to a givenimage. While useful in applications where users may be accustomed tocolor distortion, in applications where a user is not accustomed to sucha color skew, it may result in customer dissatisfaction for applicationswhere users are not accustomed to such a color skew. For example, anemployee of a firm specializing in the analysis of digital imagery maybe accustomed to such a color skew, while a private individual seekingto purchase a satellite image of an earth location of interest to themmay find such a color skew unacceptable.

Other methods for enhancing contrast in digital images may be used thatpreserve the color of the image. While such methods preserve color, theyare generally quite computer intensive and require significant amountsof additional processing as compared with a contrast stretch asdescribed above. For example, due to inadequacies in COTS stretchalgorithms, images may be stretched manually by manipulating imagehistograms to achieve the desired result. This can be a verytime-consuming, labor-intensive process. Another method of performing acolor preserving contrast stretch follows three steps. First, aprocessing system converts RGB data to a Hue Intensity Saturation (HIS)color space. Next, a contrast stretch is applied to the I (Intensity)channel within the HIS color space. Finally, the modified HIS isconverted back to the RGB color space. By adjusting the Intensitychannel in the HIS color space, the brightness of the image is enhancedwhile maintaining the hue and saturation. The image in the RGB colorspace thus has enhanced contrast while maintaining color balance. Thistechnique is reliable, however, it requires significant additionalprocessing as compared to a contrast stretch performed on RGB data aspreviously described. A major drawback to this type of stretch involvesthe significant amounts of computer processing time involved inconverting from RGB to HIS space and back.

SUMMARY OF THE INVENTION

The present invention provides a system and method to process originalDNs provided by a satellite imaging system to produce a set of colorbalanced and contrast enhanced images. The present invention enhances amultispectral digital image in a fully-automatic, systematic, anduniversal way, allows for the production of optimized enhanced digitalimage products that are cost effective and profitable, and also producesa set of quantitative surface-reflectance estimates that can be furtherprocessed by other automatic algorithms to yield specific kinds ofinformation about the objects that have been imaged. The set of colorbalanced and contrast enhanced images is referred to herein as DynamicRange Adjusted (DRA) images. The DNs in the multispectral bands may beprocessed using a relatively small amount of processing resourcesotherwise required to produce such images. Such DRA products providerelatively easy visual interpretation, and may include images in whicheach pixel is in an 8-bit format, four-band 8-bit or 24-bit RGB. Theimage may be displayed and/or printed without applying any additionalcontrast stretches.

In one embodiment, a method is provided for producing an enhanceddigital image, the method comprising: receiving a plurality of pixels ofimaging data from an imaging sensor, each of the pixels of imaging datacomprising a digital number; processing the digital number of each ofthe pixels to determine a distribution of imaging data for the pluralityof pixels, and determining the spectral radiance at top of atmosphere(TOA) of each of the plurality of pixels based on the distribution. Thereceiving step, in one embodiment, comprises receiving a plurality ofbands of pixels of imaging data from a plurality of imaging sensors. Theplurality of imaging sensors may include a blue band imaging sensor, agreen band imaging sensor, a red band imaging sensor, and anear-infrared band imaging sensor. In another embodiment, the receivingstep comprises receiving a plurality of top-of-atmosphere pixels ofimaging data from the imaging sensors, the top-of-atmosphere pixelscomprising a top-of-atmosphere digital number, and adjusting thetop-of-atmosphere digital numbers based on known sensor characteristicsof the imaging sensors. The step of determining a spectral radiance stepmay be performed independently of scene content of the digital image,and may be performed on a physical basis of the imaging data, ratherthan a purely statistical basis.

When processing the digital number, a lower distribution cutoff of saiddistribution of imaging data may be determined. The lower distributioncutoff, in an embodiment, is set at 0.1% of the cumulative distribution.In another embodiment, the lower distribution cutoff is based on a pointin the cumulative distribution at which the digital numbers areindicative of the spectral radiance of the atmospheric path, hereinafterreferred to as “path radiance.” When the lower distribution cutoff isdetermined, the method may further include subtracting a digital numberassociated with the lower distribution cutoff from each of the pluralityof pixels of imaging data. In another embodiment, the method includesdetermining the value of a digital number associated with the lowerdistribution cutoff and subtracting the digital number associated withthe lower distribution cutoff from each of the plurality of pixels ofimaging data when the digital number associated with the lowerdistribution cutoff is less than a predetermined maximum value. When thelower distribution cutoff is greater than the predetermined maximumvalue, the predetermined maximum value is subtracted from each of theplurality of pixels of imaging data.

In another embodiment, the contrast of a digital image is also enhancedby determining a median value of the input DNs, determining a targetbrightness value for the enhanced digital image, and adjusting each DNto generate an output DN, the median value of the output DNs beingsubstantially equal to the target brightness value. When multiple bandsof imaging data are present, the DNs of each band of imaging data arereceived from the plurality of imaging sensors. The median value of thereceived DNs is determined by processing the DNs of each of the bands ofimaging data to determine a distribution of DNs for each of theplurality bands of imaging data, determining a median value of each ofthe distributions of DNs, and computing an average of the median values.

In another embodiment, the present invention provides a satellite imagecomprising a plurality of pixels each having a value determined by:determining a magnitude of spectral radiance of each a plurality ofdigital numbers of raw data pixels; processing the magnitudes ofspectral radiance to determine a distribution of magnitudes of spectralradiance; and calculating the value of each of the plurality of pixelsbased on the distribution. The step of determining a magnitude ofspectral radiance may include receiving a plurality of bands of pixelsof imaging data from a plurality of imaging sensors, each pixel having adigital number representing the spectral radiance received by theassociated imaging sensor, and determining a magnitude of spectralradiance for each of the digital numbers for each of the bands ofpixels.

In another embodiment, the magnitude of spectral radiance is compensatedfor at least a portion of the digital numbers for any given band of thebands of pixels based on a known non-linear response of an imagingsensor associated with the spectral band. The compensated portion of thedigital numbers are digital numbers which are greater than a digitalnumber associated with a known response roll-off for the imaging sensor.When performing the compensation, it is first determined that a digitalnumber for a given pixel of any given band is greater than apredetermined digital number. Second, the digital numbers associatedwith the pixel from the remaining bands are determined and a compensateddigital number based on digital numbers from the other bands iscomputed.

In yet another embodiment, the present invention provides a method fortransporting a color enhanced image towards an interested entity. Themethod comprises: conveying, over a portion of a computer network, animage that includes a plurality of pixels each having a value that hasbeen determined based on a distribution of spectral radiance valuesassociated with the plurality of pixels.

Still a further embodiment of the present invention provides a methodfor producing an enhanced digital image, comprising: receiving aplurality of pixels of imaging data from an imaging sensor; determininga magnitude of spectral radiance of each the plurality of pixels ofimaging data; processing the magnitudes of spectral radiance todetermine a distribution of magnitudes of spectral radiance for theplurality of pixels; adjusting the spectral radiance of each of theplurality of pixels based on the distribution to produce a path radianceadjusted value spectral radiance; processing the adjusted value spectralradiance for each of the plurality of pixels to determine an adjusteddistribution for the plurality of pixels; assessing the adjusteddistribution to determine a range of adjusted values and a median valueof the range of adjusted values; and secondly adjusting the value of atleast a subset of the plurality of pixels to create a second adjusteddistribution wherein the median of the second adjusted distributioncorresponds with a target median and the range of the second adjusteddistribution corresponds with a target range.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic illustration of a satellite in an earth orbitobtaining an image of the earth;

FIG. 2 is a block diagram representation of a system of an embodiment ofthe present invention;

FIG. 3 is a diagrammatic illustration of light scattering through theearth atmosphere;

FIG. 4 is a flow chart illustration of the operational steps forcollecting, processing and delivering a digital image for an embodimentof the present invention;

FIG. 5 is a flow chart illustration of the operational steps for colorcorrection of a digital image for an embodiment of the presentinvention;

FIG. 6 is a flow chart illustration of the operational steps forcontrast enhancement of a digital image for an embodiment of the presentinvention;

FIG. 7 is a diagrammatic illustration of linear and non-linear sensorresponses for different imaging sensors; and

FIG. 8 is a flow chart illustration of the operational steps forcompensation of a known sensor non-linearity for an embodiment of thepresent invention.

DETAILED DESCRIPTION

The present invention provides a digital image that may be displayedwith no further manipulation on the part of the customer or end user ofthe digital image. The present invention may also provide a set ofquantitative surface-reflectance estimates that can be further processedby other automatic algorithms to yield specific kinds of informationabout the objects that have been imaged. When used to provide a digitalimage, a digital image collected at a satellite is spectral balanced andcontrast stretched on a physical basis rather than a subjective one. Acustomer may use the image without additional tools, expertise, or timeto perform complex digital image processing manipulations. In additionto serving the basic commercial customer, the present invention may beuseful for the remote sensing expert to aid in their application. Thepresent invention also provides an acceptable contrast stretch andspectral balance over a very wide variety of scene content. Thealgorithm uses known physical characteristics of the imaging sensors toestimate the actual spectral radiance measured by the satellite. A firstorder correction for major atmospheric effects is then applied. Finallya contrast stretch is applied to enhance the visual contrast of theimage without disturbing the spectral balance. In this fashion the DRAalgorithm automatically provides a visually appealing image based onstandard human color perception principles.

Having generally described the process for producing an image, variousembodiments of the present invention are described in greater detail.Referring to FIG. 1, an illustration of a satellite 100 orbiting aplanet 104 is described. At the outset, it is noted that, when referringto the earth herein, reference is made to any celestial body of which itmay be desirable to acquire images or other remote sensing information.Furthermore, when referring to a satellite herein, reference is made toany spacecraft, satellite, and/or aircraft capable of acquiring imagesor other remote sensing information. Furthermore, the system describedherein may also be applied to other imaging systems, including imagingsystems located on the earth or in space that acquire images of othercelestial bodies. It is also noted that none of the drawing figurescontained herein are drawn to scale, and that such figures are for thepurposes of discussion and illustration only.

As illustrated in FIG. 1, the satellite 100 orbits the earth 104following orbital path 108. An imaging system aboard the satellite 100is capable of acquiring an image 112 that includes a portion the surfaceof the earth 104. The image 112 is comprised of a plurality of pixels.Furthermore, the satellite 100 may collect images 112 in a number ofspectral bands. In one embodiment, the imaging system aboard satellite100 collects four bands of electromagnetic energy, namely, a red band, agreen band, a blue band, and a near infrared band. Each band iscollected by a separate imaging sensor that is adapted to collectelectromagnetic radiation. Data from the sensors is collected,processed, and images are produced. Each band consists of digitalnumbers (DNs) on an 8-bit or 11-bit radiometric brightness scale. TheDNs from each band are processed to generate an image that is useful forthe application required by a user. Images collected from the satellite100 may be used in a number of applications, including both commercialand non-commercial applications.

Referring now to FIG. 2, a block diagram representation of an imagecollection and distribution system 120. In this embodiment, thesatellite 100 includes a number of systems, including power/positioningsystems 124, a transmit/receive system 128, and an imaging system 132.Such a satellite and associated systems 124 are well known in the art,and therefore are not described in detail herein as it is sufficient tosay that the satellite 100 receives power and may be positioned tocollect desired images and transmit/receive data to/from a groundlocation and/or other satellite systems. The imaging system 132, in isembodiment, includes four imaging sensors that collect electromagneticenergy received at the sensor within a band of electromagnetic energy.In this embodiment, the imaging system 132 includes a blue sensor 140, agreen sensor 144, a red sensor 148, and a near-infrared (NIR) sensor152. Each of these sensors 140-152 collect electromagnetic energyfalling within preset energy bands that is received at the sensor. Theimaging sensors 140-152, in this embodiment, include charge coupleddevice (CCD) arrays and associated optics to collect electromagneticenergy and focus the energy at the CCD arrays. The CCD arrays areconfigured to collect energy from a specific energy band by a mass ofoptical filters. The sensors 140-152 also include electronics to samplethe CCD arrays and output a digital number (DN) that is proportional tothe amount of energy collected at the CCD array. Each CCD array includesa number of pixels, and the imaging system operates as a pushbroomimaging system. Thus, a plurality DNs for each pixel are output from theimaging system to the transmit/receive system 128.

The satellite 100 transmits and receives data from a ground station 160.The ground station 160 of this embodiment includes a transmit/receivesystem 164, a data storage system 168, a control system 172, and acommunication system 176. In one embodiment, a number of ground stations160 exist and are able to communicate with the satellite 100 throughoutdifferent portions of the satellite 100 orbit. The transmit/receivesystem 164 is used to send and receive data to and from the satellite100. The data storage system 168 may be used to store image datacollected by the imaging system 132 and sent from the satellite 100 tothe ground station 160. The control system 172, in one embodiment, isused for satellite control and transmits/receives control informationthrough the transmit/receive system 164 to/from the satellite 100. Thecommunication system 176 is used for communications between the groundstation 160 and one or more data centers 180. The data center 180includes a communication system 184, a data storage system 188, and animage processing system 192. The image processing system 192 processesthe data from the imaging system 132 and provides a digital image to oneor more user(s) 196. The operation of the image processing system 192will be described in greater detail below. Alternatively, the image datareceived from the satellite 100 at the ground station 160 may be sentfrom the ground station 160 to a user 196 directly. The image data maybe processed by the user using one or more techniques described hereinto accommodate the user's needs.

Referring now to FIG. 3, an illustration of an imaging system collectingsensing data is now described. The satellite 100, as illustrated in FIG.3, receives radiation from the earth 104. The radiation received at thesatellite 100 has three components. L_(direct) 200 is the surfacereflected attenuated solar radiation, L_(upwelling) 204 is theup-scattered path radiance, and L_(downwelling) 208 is the downscattered “sky” radiance that has been reflected by the surface into thesensor. Thus, the total radiation, L_(total) received at the satellite100 can be expressed as follows:L _(total)(λ)=L _(direct)(λ)+L _(upwelling)(λ)+L _(downwelling)(λ)where λ indicates a dependence on wavelength. The three terms in thisequation are the result of a complex radiative transfer process in theatmosphere as well as reflection by surface materials and λ indicates adependence on wavelength. Note that the L_(upwelling) 204 andL_(downwelling) 208 rays may undergo multiple scattering events beforeentering the sensor. Furthermore, atmospheric interactions may alsoinclude absorption of radiance.

The atmospheric scattering contributions, which effect the L_(upwelling)204 and L_(downwelling) 208 terms, are generally governed by Rayleighand Mie scattering. Rayleigh scattering is caused by the variousmolecular species in the atmosphere and is proportional to λ⁻⁴. This isa selective scattering process that affects shorter wavelength radiancesmore than longer wavelength radiances. Because of the strong wavelengthdependence of Rayleigh scattering, blue light (shorter wavelength) isscattered much more than red light (longer wavelength). This scatteringmechanism generally causes the path radiance signal to be much higher inthe blue channel 140 (FIG. 2) than in the near IR channel 152 (FIG. 2).Mie scattering is often called “aerosol” scattering. Similar to Rayleighscattering, Mie scattering is also wavelength dependent (roughlyproportional to λ⁻¹). Mie scattering is caused by scattering off largesized atmospheric constituents like smoke and haze. Both of thesescattering mechanisms contribute to atmospheric scattering ofelectromagnetic radiation illustrated in FIG. 2.

Referring now to FIG. 4, the operational steps performed in imagecollection, processing, and delivery are now described for an embodimentof the invention. Initially, as indicated at block 220, the image iscollected. As discussed above with respect to FIG. 2, in an embodimentelectromagnetic radiation is collected at an imaging system aboard asatellite, with the imaging system providing data related to the totalradiance received at certain bands associated with certain sensors. Thedata provided by the imaging system is used to produce a digital image.This data, at block 224, is spectrally corrected. As discussed above,the color perceived by a user viewing an image produced from a satelliteimaging system may be different than the color of an object that wouldbe observed from a location relatively close to the object. Thisdifference in perceived color is due to the reflected light received atthe satellite imaging system being scattered through the atmosphere. Thespectral correction of block 224, in an embodiment, adjusts the spectralbalance of an image based on known sensor information and also furtheradjusts the spectral balance of an image in order to partiallycompensate for the atmospheric scattering of the light as it passesthrough the atmosphere. At block 228, the contrast of the image isstretched. The contrast stretch, in an embodiment, is used to provideadditional perceived contrast between features within the image. Atblock 232, the image is delivered to one or more user(s) and/orapplication(s), referred to as a receiver. In one embodiment, theimage(s) are transmitted to the receiver by conveying the images overthe Internet. Typically, an image is conveyed in a compressed format.Once received, the receiver is able to display an image of the earthlocation having a visually acceptable color and contrast. It is alsopossible to convey the image(s) to the receiver in other ways. Forinstance, the image(s) can be recorded on a magnetic disk, CD, tape,solid-state memory, or other recording medium and shipped to thereceiver. It is also possible to simply produce a hard copy of an imageand then ship the hardcopy to the receiver. The hard copy can also befaxed, scanned, or otherwise electronically sent to the receiver.

Referring now to FIG. 5, the operational steps for performing spectralcorrection are described for an embodiment of the present invention.Initially, at block 236, spectral information is collected. As notedpreviously, spectral information is collected, in an embodiment, usingCCD detectors within an imaging system. Referring again to FIG. 2, theimaging system 132, comprises sensors 140-152 that comprise CCDdetectors for each spectral band. The sensors 140-152 collect spectralinformation in their respective bands. The radiometric dynamic range ofeach image, in one embodiment, is 11 bits, although any dynamic rangemay be used. In this embodiment, the digital number produced from eachelement within a CCD detector has a range from 1 to 2047, with zerosused for backfill. As is understood, CCD detectors collect spectralinformation and output the digital number associated with the amount ofspectral radiation received at the detector. Numerous methods exist forthe collection and sampling of the spectral information within a CCDdetector to produce the digital number, the details of which are wellunderstood in the art.

Referring again to FIG. 5, following the collection of spectralinformation, radiometric correction coefficients associated with thesensor are applied to the spectral information, as noted at block 240.Within the imaging sensor, each CCD detector has a set of coefficients,called radiometric correction coefficients, which represent anapproximate, though not always, linear response between the amount ofspectral radiance received at the detector and the DN output of thedetector. The response of each detector, even though they may bemanufactured at the same time using the same technique, commonly differsfrom detector to detector. If left uncorrected the resulting image wouldcontain bands and stripes corresponding to different responses from eachdetector. By applying the radiometric correction coefficients to theoutput of the detector the response of each detector is corrected. Theprocess of correcting the response of each detector is termed“radiometric correction”. In this process, a set of linear calibrationcoefficients (corresponding to a gain and a dark offset) are measuredfor each detector and then applied to the image data to provideconsistency between detectors. In one embodiment, the calibrationcoefficients are estimated on a regular basis on-orbit using standardcalibration techniques that are well known.

Following the radiometric correction, the next step in the spectralcorrection of imaging data is the conversion of the radiometricallycorrected data to spectral radiance, as noted at block 244. As mentionedpreviously, imaging data is collected in several bands. This conversionis applied to each spectral band as follows:

${L_{\lambda,{TOA}}({band})} = {{{{DN}({band})} \cdot \frac{K({band})}{\Delta({band})}}\left( \frac{W}{{m^{2} \cdot {ster} \cdot \mu}\; m} \right)}$where L_(λ,TOA) (band) is the spectral radiance of the band, DN(band) isthe radiometrically corrected Digital Number (DN) of the band, K(band)is the band integrated radiance conversion factor (W m⁻² ster⁻¹ DN⁻¹),and Δ(band) is the effective bandwidth (μm) of the band. The K factorsare factors that are provided for the imaging system and are describedin more detail below. Once the correction listed in this equation isperformed, the image data represents what the sensor measures, withinradiometric error estimates. L_(λ,TOA) is referred to as the Top OfAtmosphere (TOA) spectral radiance.

In one embodiment, the K factors are band dependant factors that convertthe input corrected DN data to band integrated radiance (in units of Wm⁻² sr⁻¹). When the K factor is divided by the effective bandwidth ofthe spectral channel, the result, which converts input DN's TOA spectralradiance, is termed the kappa factor. The kappa factor, since it isderived from the original k factor, is also band dependant.

An example of K-factors, effective bandwidths of the sensor channels,and kappa factors are listed in Table A-1.

TABLE A-1 Spectral Radiance Conversion Factors for spectral bands. Δλ,Effective Kappa factor Band K factor (W m⁻² sr⁻¹⁾ Bandwidth (μm) (Wm⁻²-sr⁻¹ μm⁻¹) Blue 1.604120e−02 0.068 0.2359 Green 1.438470e−02 0.0990.1453 Red 1.267350e−02 0.071 0.1785 NIR 1.542420e−02 0.114 0.1353

The conversion of the radiometrically corrected DNs to L_(λ,TOA) maythus also be performed by taking the product of kappa and the DN. Theconversion to L_(λ,TOA) is straightforward, however, because of thesmall magnitude of the kappa coefficients (Table A-1), the directapplication of the kappa coefficients of this example to the DN datacollapses the dynamic numeric range of the imagery data into a smallerrange. In the example of Table A-1, an original DN of 2047 would convertto 482.9 (blue band), which, when handled as integer numbers, isequivalent to a loss of radiometric resolution if the data is kept ininteger format.

In order to provide enhanced results, in one embodiment each kappafactor is converted to a dimensionless ratio, and these ratios areapplied to the DN data. This process preserves the relative spectralbalances among the bands and preserves, and may in some cases enhance,the radiometric range of the adjusted DN data. The transformed factorsare termed “adjusted kappa” factors and are computed as follows:

${Kappa}_{AVE} = {\frac{1}{num\_ bands}{\sum\limits_{band}{{Kappa}({band})}}}$${Kappa}_{ADJ} = \frac{{Kappa}({band})}{{Kappa}_{AVE}}$

Adjusted kappa factors using the example of table A-1 for each band arelisted in table A-2. Note that they are non-dimensional.

TABLE A-2 Adjusted kappa factors for the spectral bands. Band AdjustedKappa Blue 1.452586 Green 0.894704 Red 1.099137 NIR 0.833128

Following the conversion to spectral radiance, the interaction of solarradiation with the atmosphere is taken into account. The atmosphericscattering contributions, which effect the L_(upwelling) andL_(downwelling) terms, as discussed above, are generally governed byRayleigh and Mie scattering. In one embodiment, a full atmosphericcorrection may take into account all three terms of L_(upwelling),L_(downwelling), and L_(direct). This is a relatively complextransformation, requiring an atmospheric radiative transfer modelapplication such as MODTRAN. These programs provide a full radiativetransfer model capable of modeling the interactions of radiant energywith the Earth's atmosphere to a high degree of accuracy if the physicalstate of the atmosphere is well known. This process is generally verytime consuming owing to the complex nature of the desired correction andthe accuracy is dependent on the exact knowledge of the atmosphericconstituents for each scene.

In the embodiment of FIG. 5, as noted at block 248, a path radiance isestimated by noting the “dark target” DN value in each spectral band.This embodiment thus estimates the path radiance in each band. This DNthen becomes a DN offset value. This offset is an estimate of the totalamount of radiation entering the sensor that is not reflected into thesensor by the Earth's surface. An estimation of the DN associated withpath radiance for each of the spectral bands of imaging data (blue,green, red, and NIR) is generated. At block 252, the DN offset value isapplied to L_(λ,TOA) to generate a path-corrected TOA spectral radiance.The path-corrected TOA spectral radiances of the scene in a givenspectral band are obtained by subtracting the path-radiance value (theDN offset value) for that band from the total spectral radiances forthat band. In this embodiment, spectral properties based on TOApath-corrected spectral radiances are similar to spectral propertiesbased on the spectral radiances of surface materials observed just abovethe surface. The only property that changes significantly from surfaceto TOA is the overall intensity of the three bands. In other words, ifan observer at TOA could see path-corrected spectral radiances of anobject on the Earth, he or she would see similar colors from close rangeat the surface of the Earth (on a cloudless, relatively haze-free daywith the same solar irradiance geometry).

In one embodiment, the dark target DN value is estimated automaticallyfor each band by identifying the 0.1% point in the cumulativedistribution histogram for the band. This low-end cutoff represents thefirst DN in the band where an appreciable signal from surface objects ismeasured, and it is an estimate of the path radiance contribution to theband. It should be noted that this low-end cutoff at the 0.1% point inthe cumulative distribution histogram is merely one example ofestimating the dark target DN value. Other points in the distributionmay be selected, the dark target DN value may be manually selected by auser, or the point in the distribution may be dynamically adjusted basedon the scene content and distribution histogram. The low-end cutoffpoint is computed with respect to the adjusted DN values of thisembodiment. Subtracting out this adjusted DN value from each pixel isequivalent to subtracting out the path radiance contribution from theoverall TOA spectral radiance.

Once the low-end cutoff point in the image cumulative distributionhistogram is identified, the DN associated with the low-end cutoff isset as the dark target DN value, also referred to as DN_path(band). ThePath Radiance Corrected (PRC) DN can be computed as follows:DN_path_(ADJ)(band)=DN_path(band)·Kappa_(ADJ)(band)DN_(PRC)(band,pixel)=DNA_(ADJ)(band, pixel)−DN_path_(ADJ)(band)

In one embodiment, the value of DN_path_(ADJ)(band) is determined foreach spectral-band of imaging data. For example, if four bands ofimagery are collected, a value is produced for DN_path_(ADJ)(band_(—)1),DN_path_(ADJ)(band_(—)2), DN_path_(ADJ)(band_(—)3), andDN_path_(ADJ)(band_(—)4) representing the blue-light, green-light,red-light, and NIR bands, respectively.

Each of these separately estimated DN_Path_(ADJ)(band) values is alsorelated, in a direct linear way, to L_(λ,TOA) through thestraightforward application of the band-specific K_Factor(band) andA(band) coefficients. That is,L_(λ,TOA)(band_(—)1)=DN_path_(ADJ)(band_(—)1)×K_Factor(band_(—)1)/Δ(band_(—)1)L_(λ,TOA)(band_(—)2)=DN_path_(ADJ)(band_(—)2)×K_Factor(band_(—)2)/Δ(band_(—)2)L_(λ,TOA)(band_(—)3)=DN_path_(ADJ)(band_(—)3)×K_Factor(band_(—)3)Δ(band_(—)3)L_(λ,TOA)(band-4)=DN_path_(ADJ)(band_(—)4)×K_Factor(band_(—)4)/Δ(band_(—)4)

As discussed above, the combination of K_Factor/Δ is referred to as thekappa factor.

Using the example from Table A-1, values of kappa for each band are:

KAPPA_(—)1=0.2359 Watts per square meter per steradian per micrometerper DN

KAPPA_(—)2=0.1453 Watts per square meter per steradian per micrometerper DN

KAPPA_(—)3=0.1785 Watts per square meter per steradian per micrometerper DN

KAPPA_(—)4=0.1353 Watts per square meter per steradian per micrometerper DN

In turn, each L_(λ,TOA)(band) value can be converted to an apparentreflectance factor at the top of the atmosphere (RFTOA), as seen by anobserver (RFTOA_path_band).

To calculate a value of RFTOA from each value of L_(λ,TOA), three itemsof information are required:

The spectral irradiance of the sun at TOA (SISUNTOA),

The elevation angle, in degrees, of the sun (SUNELEV), and

The earth-sun distance, in Astronomical Units (A.U.), (D)

SISUNTOA is a known constant for each spectral band. SUNELEV and D arethe same for each spectral band. SUNELEV varies from place to place onthe earth and from date to date (day of year, DOY). D varies with DOYonly.

With these parameters, the conversion equation from SRTOA to RFTOA is asfollows:RFTOA=L _(λ,TOA)×pi×D×D×100%/[SISUNTOA×SIN(SUNELEV)]where pi=3.1415927 . . . .

Values of SISUNTOA for each band are, in one example:

SISUNTOA_(—)1=1930.11 Watts per square meter per micrometer

SISUNTOA_(—)2=1842.47 Watts per square meter per micrometer

SISUNTOA_(—)3=1561.61 Watts per square meter per micrometer

SISUNTOA_(—)4=1097.64 Watts per square meter per micrometer

In one embodiment, larger errors are associated with the estimation ofDN_path_(ADJ) (band_(—)4) than for the other DN_path_(ADJ)(band) values.This is due to the fact that in some scenes, there are no non-reflectingsurface objects in the NIR. But, in the other bands, especially in thered-light band, dark (non-reflecting) objects are commonly present in ascene, namely, dense green vegetation. RFTOA generally decreases withwavelength from Band 1 to Band 4. However, due to the various conversionfactors (i.e., KAPPA and SISUNTOA), the relative values ofDN_path_(ADJ)(band) will normally not exhibit any such simple pattern ofdecrease with wavelength. However, since D and SIN(SUNELEV) are the samefor all bands (i.e., wavelength does not affect these), the ratio ofRFTOA_path_(—)4 divided by RFTOA_path_(—)3, called RFRATIO43, is relatedto the ratio of DN_path_(—)4 divided by DN_path_(—)3, called DNRATIO43,in a fixed way, as follows:RFRATIO43=DNRATIO43×1.078381Inversely,DNRATIO43=0.927316×RFRATIO43

It is generally expected that RFRATIO43 is 0.8 or less. If RFRATIO43 isgreater than 0.8, then, in one embodiment, it is assumed that theRFTOA_path_(—)4 value is too high, and that no totally absorbing bodywas present in the scene as seen in the NIR band. In this case, in oneembodiment, an estimate is selected as the value of RFTOA_path_(—)4 as:RFTOA_path_(—)4_better=RFTOA_path_(—)4×0.8

In terms of DN_path_(ADJ) values, DNRATIO43 is normally 0.741853 or lessin an embodiment. If DNRATIO43 is greater than this value, an estimateis selected as the value of DN_path_(ADJ)(band_(—)4) as:DN_path_(ADJ) _(—) better(band_(—)4)=DN_path_(ADJ)(band_(—)3)×0.741853

In a scene with 100% cloud cover, as is often processed by theprocessing system, the above method identifies a path radianceequivalent (low-end) DN value that is much too high, resulting in anincorrect spectral transformation. To avoid this problem, in oneembodiment, path radiance values are not allowed to exceed to anabsolute maximum value. A value of 400 DNs (11 bit input data) is set asthe maximum path radiance value for every band in one embodiment,although a different value may be used so long as the spectraltransformation is acceptable. Actual values of path-radiance DNs are notthe same for all bands and tend to be higher for blue light and greenlight than for red light and NIR. Nevertheless, under cloudy conditions,actual scene DNs are generally quite high (above 1500). In such cases,minor differences among path-radiance DN estimates have relatively smalleffects on the perceived color of the transformed imagery.

In one embodiment, a Look Up Table (LUT) is computed for each bandcorresponding to the DN_(PRC). The DN_(PRC) values are generated on aband specific basis using the above equations. The DN_(orig) value inthe equations is simply in the integer range of the input data such as1-2047 for 11 bit data, and 1-255 for 8 bit data. In this embodiment,intermediate computations are kept in floating point format to avoidround off and overflow errors during the calculation. Also, the resultsof the LUT are not allowed to exceed the allowed range of the output DNvalues. This can be accomplished by testing the DN value for values thatfall below 1 or above the maximum DN output, and resetting the value to1 or to the maximum accordingly.

Once the data has been converted to TOA path-radiance corrected spectralradiances, the visual contrast is enhanced to facilitate visualinterpretation of the imagery. FIG. 6 illustrates the operational stepsfor enhancing contrast for one embodiment of the present invention. Thecontrast enhancement technique of this embodiment differs from many COTScontrast stretches in that it is a color-hue-and-color-saturationpreserving contrast stretch. The color preservation property of thisstretch is beneficial since the spectral correction operationspreviously described are designed to correct the imagery in terms of hueand saturation. Thus, the desire in making contrast improvements is toonly increase its brightness (intensity) while not affecting thecolor-hue or color-saturation.

Referring to FIG. 6, initially, a determination is made, noted at block260, for the upper and lower percentile limits of the of the imagehistogram for each band. In one embodiment, the lower percentile limitis set to 1% of the cumulative distribution of the image histogram, andthe upper percentile limit is set to 99% of the cumulative distributionof the image histogram. Any percentile cutoff can be used for thisdetermination, such as 2%, which corresponds to the 98% point in thecumulative histogram. However, a 2% cutoff may cut off too much of theupper end of the DN histogram, resulting in significant loss of detailover bright objects. In such a situation, a 0.5 percentile cutoff may beused, which corresponds to the 0.5% and 99.5% points in the imagehistogram. As will be understood, any value for the percentile cutoffmay be used and, in one embodiment, the contrast enhancement algorithmis implemented with these parameters as configurable values that may beconfigured by a user.

At block 264, the DN corresponding to the upper and lower percentilelimits for each band are identified. The cutoff value of the upper DN(hi_dn) and lower DN (lo_dn_(ADJ)) is selected at block 268. Theselection is made by first identifying the highest DN corresponding tothe high percentile cutoff for each band in the image, and the largestof these values is selected at block 268. Similarly, the selection oflo_dn is made by identifying the lowest DN corresponding to the lowerpercentile cutoff for all bands in the image. The values of hi_dn andlo_dn are selected, in this embodiment, according to the followingequations:hi_dn=MAX(hi_dn(band))lo_dn=MIN(lo_dn(band))

Once the values of hi_dn and lo_dn are identified, they are thenconverted to adjusted DNs using the following equations:hi_dn_(ADJ)=hi_dn×Kappa(band)/Kappa_(AVE)lo_dn_(ADJ)=lo_dn×Kappa(band)/Kappa_(AVE)

The value of the band used in the above equations is the band thatcontains the hi_dn and the band that contains lo_dn, respectively.

At block 272, the average target brightness value for the image isidentified. The value of the average target brightness has a significantimpact on the visual appearance of the image, and is often open tosubjective interpretation. Thus, in an embodiment, the value of theaverage target brightness is configurable by a user. In otherembodiments, the value is the average target brightness is preset to avalue that is likely to produce a visually acceptable image. At block276, the median of the modified DN values for each band in the image iscomputed. This median is computed by determining the median value of thecumulative image histogram for each band. At block 280, the average ofthe medians from each band is computed. In one embodiment the average ofthe medians from each band is computed according to the followingequation:

${{avg\_ median}{\_ dn}} = {\frac{1}{nbands}{\sum\limits_{{band} = 1}^{nbands}{{median}({band})}}}$where median(band) indicates the median value of the original image forthe given band. Following the computation of the average of the mediansfor each band, a gamma factor (G) is computed, according to block 284.The value of G is determined by using the average target brightnessvalue for the image and computing the value of G needed to bring theoverall median brightness of the image up to the target brightnessvalue. In one embodiment, G is computed based on the desired targetbrightness and the average brightness according to the followingequation:

$G = \frac{\log\left( \frac{{{avg\_ median}{\_ dn}} - {lo\_ dn}}{hi\_ dn} \right)}{\log\left( \frac{trgt\_ dn}{max\_ dn} \right)}$where avg_median_dn is the average of the median modified DN values foreach band in the image; max_dn is the maximum DN value of the imageoutput (i.e. 255 for 8-bit products or 2047 for 11-bit products); andtrgt_dn is the DN value of the target brightness in the image.

In the embodiment of FIG. 6, the value of gamma is limited to ensurethat an exaggerated stretch is not applied to the imagery. At block 288,it is determined if the value of gamma is outside of these limits. Ifthe value of gamma is outside the limits, the value for gamma is set atthe appropriate high or low limit, as noted at block 292. In oneembodiment, gamma values are set to have a limit of no greater than 2.0and no less than 0.5. In other embodiments, these limits are implementedas ran-time configurable values. Following the determination of thegamma value, the stretch is applied, as noted at block 296. The stretchequation, in an embodiment, is:

${{DN}_{stetch}\left( {{band},{pixel}} \right)} = {\left\lbrack \frac{\begin{matrix}{{{DN}_{PRC}\left( {{band},{pixel}} \right)} -} \\{lo\_ dn}_{ADJ}\end{matrix}}{\begin{matrix}{{hi\_ dn}_{ADJ} -} \\{lo\_ dn}_{ADJ}\end{matrix}} \right\rbrack^{1/G} \times {hi\_ value}}$where DN_(PRC) is the TOA path radiance corrected DN, lo_dn_(ADJ) is thelowest cutoff from all the bands, hi_dn_(ADJ) is the highest cutoff fromall the bands, G is the gamma factor, and hi_value is the maximum DNdesired in the output image. The stretch equation is applied to eachpixel in each band to produce a stretched DN value for the pixel. Thistechnique increases the intensity of each channel without materiallychanging the hue and saturation of the imagery. The resulting imageproduct maintains the correct spectral balance of the imagery whileproviding an acceptable level of contrast enhancement. The result is avisually appealing image that requires no manipulation for properdisplay. The results of the algorithm generally do not depend on scenecontent, and interpretation of the product requires little or noexperimentation or “guessing” by the remote sensing analyst.

In one embodiment, as mentioned above, a lookup table is used todetermine a path radiance corrected DN for each pixel in a band. In thisembodiment, the original pixel values (for each band) are read in fromthe original image. These DNs are used as an index into the DN_(PRC)lookup tables to retrieve the value of DN_(PRC) in the stretch equation.The stretch is then applied and the pixel value is written out to theimage. In one embodiment, the value of the DN for each pixel isevaluated to verify that the DN value is not outside of certain limitsfollowing the application of the stretch equation. For example, if theDNprc-lo_dn is less than 0, then DNstretch is set to 1. In thisembodiment, the DN value of zero is reserved for blackfill. Similarly,if DNprc is greater than the hi_value, then DNstretch is set to be thehi_value.

In another embodiment of the present invention, spectral compensationmay be generated for known imaging sensor non-linearities. As is known,each band of imagery has an associated imaging sensor that receivesradiance from the particular band of interest. In some cases, the sensormay have a non-linear response, and provide DN values that reach amaximum limit when the amount of radiance received at the sensor isabove a threshold limit. Referring to FIG. 7, the output of threespectral bands is illustrated as a function of radiance received at thesensor associated with the respective spectral band. In this example, afirst band has a linear response throughout the full range of DNs, asindicated by line 310. Similarly, a second band also has a linearresponse throughout the full range of DNs, as indicated by line 314.However, a third band has a non-linearity, as indicated by line 318. Inthis example, the third band has a linear response up to a certainamount of radiance received, and the output of the sensor is non-linearbeyond this points. This point is referred to herein as a roll-offpoint, and is indicated at point 322 in FIG. 7. In the example of FIG.7, the third sensor reaches a maximum output at point 326, after whichthe output of the sensor is the same DN regardless of any additionalradiance received at the sensor. The output of the non-linear sensor iscorrected in one embodiment using a technique referred to as “spectralfeathering.”

In an embodiment, spectral feathering is performed according to the flowchart illustration of FIG. 8. Initially, at block 330, is it determinedif there is a known non-linearity of a sensor band. If there is no knownnon-linearity, no compensation is required, and the operations arecomplete, as indicated at block 334. If there is a known non-linearity,it is determined if the pixel within the band has a DN value that isgreater than the DN value associated with the roll-off point, as notedat block 338. If the pixel value is less than the roll-off value, nocompensation is required and the operations are done as noted at block334. If the pixel DN value is greater than the roll-off value, the DNvalue of the pixel is compensated based on the pixel DN values of theother bands, as indicated at block 342. In one embodiment, thecompensated pixel value is computed based on an average of the remainingbands DN values. Following the compensation, the compensated pixel DNvalue is output for the non-linear band, as indicated at block 346.

In one embodiment, a spectral band (denoted by “band”) has a knownnon-linearity. In this embodiment, for a particular pixel (denoted by“pixel”), it is determined if the DN value of the pixel in the band isgreater than the rolloff value for the band (denoted by “band_rolloff”).If so, the pixel requires spectral compensation. In this case, first,the amount of compensation required to adjust the pixel is determined asfollows:

${fraction} = \frac{{band\_ lmt} - {{input\_ dn}\left( {{pixel},{band}} \right)}}{{band\_ lmt} - {band\_ rolloff}}$where band_lmt is the DN value beyond which the sensor becomesnon-linear, band_rolloff is the DN corresponding to the beginning of thenon-linear behavior, and input_dn(pixel,band) indicates the DN valueassociated with the given pixel in the band that requires compensation.The values of fraction are bounded as follows to provide a numericallyconsistent result:if (fraction<0) then fraction=1if (fraction>1) then fraction=1

Physically the value of fraction represents how much compensation is tobe applied to the pixel. A value of zero indicates no compensation,while a value of one indicates full compensation. In order to compensatethe pixel for any known non-linearity of detector response, pixel valuesfrom adjacent spectral bands are used. The average output value of theadjacent spectral bands is computed according to the following equation:

${{ave\_ dn}{\_ out}} = \frac{\begin{matrix}{{{output\_ dn}\left( {{pixel},{{band} + 1}} \right)} +} \\{{output\_ dn}\left( {{pixel},{{band} - 1}} \right)}\end{matrix}}{2.0}$where output_dn(pixel,band+1) indicates the output DN value of thespectrally adjacent band of longer wavelength than the band to becompensated, and output_dn(pixel,band−1) indicates the output DN valueof the spectrally adjacent band of shorter wavelength than the band tobe compensated. In the situation where the band to be compensated doesnot lie spectrally between any sensor bands, two bands of either longeror shorter wavelength than the band to be compensated may be used. Thefinal compensated pixel value is determined as follows:comp_value(pixel,band)=output_value(pixel,band)×(1−fraction)+ave_dn_out*fractionwhere output_value(pixel,band) is the uncompensated DN corresponding tothe pixel in the band that requires compensation, fraction is computedabove, and ave_dn_out is computed above. In this manner the pixel valueis spectrally compensated in a smoothly varying manner using spectralinformation from adjacent spectral bands. This technique is referred toas “spectral feathering.”

While the invention has been particularly shown and described withreference to a preferred embodiment thereof, it will be understood bythose skilled in the art that various other changes in the form anddetails may be made without departing from the spirit and scope of theinvention.

1. A method for enhancing an image generated using an imaging system,the imaging system comprising a plurality of imaging sensors, includinga first imaging sensor and a second imaging sensor, the methodcomprising: receiving a first digital number at an image correctionsystem, wherein the first digital number is a representation of abrightness value of a pixel for the first imaging sensor; receiving asecond digital number at the image correction system, wherein the seconddigital number is a representation of a brightness value of the pixelfor the second imaging sensor; determining, using the image correctionsystem and the first digital number, whether to correct for a non-linearresponse of the first sensor to an amount of radiance received by thefirst sensor, wherein said determining comprises identifying a roll-offpoint for the first sensor, wherein the first sensor has a substantiallynon-linear response to an amount of radiance it receives after theroll-off point is met; and generating, using the image correctionsystem, a final digital number based on the first digital number and thesecond digital number, wherein the final digital number is a correctedrepresentation of the brightness value of the pixel for the firstimaging sensor, wherein said generating step is performed if the firstdigital number has a value greater than a digital number associated withthe roll-off point.
 2. The method of claim 1, further comprisingreplacing the first digital number with the final digital number.
 3. Themethod of claim 1, further comprising using the imaging system to createthe first digital number in response to an amount of radiance receivedby the first sensor.
 4. The method of claim 1, wherein the final digitalnumber is based on digital numbers generated by the plurality of imagingsensors other than the first sensor.
 5. The method of claim 4, whereingenerating the final digital number further comprises: calculating aratio corresponding to an amount of compensation for the first digitalnumber; calculating an average of the digital numbers generated by theplurality of imaging sensors other than the first sensor; andcalculating the final digital number based on the ratio and the average.6. A method for enhancing an image generated using an imaging system,the imaging system comprising a plurality of imaging sensors, includinga first imaging sensor and a second imaging sensor, the methodcomprising: receiving a first digital number at an image correctionsystem, wherein the first digital number is a representation of abrightness value of a pixel for the first imaging sensor; receiving asecond digital number at the image correction system, wherein the seconddigital number is a representation of a brightness value of the pixelfor the second imaging sensor; and generating, using the imagecorrection system, a final digital number based on the first digitalnumber and the second digital number, wherein the final digital numberis a corrected representation of the brightness value of the pixel forthe first imaging sensor and is based on digital numbers generated bythe plurality of imaging sensors other than the first sensor; whereingenerating the final digital number further comprises: calculating aratio corresponding to an amount of compensation for the first digitalcalculating an average of the digital numbers generated by the pluralityof imaging sensors other than the first sensor; and calculating thefinal digital number based on the ratio and the average, wherein theratio is defined by the following formula:${ratio} = \frac{{band\_ lmt} - {{first\_ digital}{\_ number}}}{{band\_ lmt} - {rolloff\_ point}}$wherein band_lint is a maximum digital number output by the firstsensor, regardless of an amount of radiance received by the firstsensor, first_digital number is the first digital number, androlloff_point is a digital number representing a roll-off point for thefirst sensor, wherein the first sensor has a substantially non-linearresponse to an amount of radiance it receives after the roll-off pointis met.
 7. The method of claim 6, wherein, if the computed value of theratio is greater than 1 or less than 0, then the value of the ratio ischanged to
 1. 8. The method of claim 6, wherein the average is definedby the following formula: ${average} = \frac{\begin{matrix}{{{output\_ dn}\left( {{pixel},{{band} + 1}} \right)} +} \\{{output\_ dn}\left( {{pixel},{{band} - 1}} \right)}\end{matrix}}{2.0}$ wherein band is a spectral band sensed by the firstsensor, output dn(pixel, band+1) is a digital number corresponding tothe pixel that is output by a sensor for a band with a longer wavelengththan band, and output dn(pixel, band−1) is a digital numbercorresponding to the pixel that is output by a sensor for a band with ashorter wavelength than band.
 9. The method of claim 8, wherein thefinal digital number is calculated according to the following formula:final_digital_number=first_digital_number*(1−ratio)+average*ratio. 10.The method of claim 1, further comprising applying, using the imagecorrection system, a same contrast enhancement stretch to each pixel ineach band of the image to produce an image with enhanced contrast andmaintained color balance.
 11. An image comprising a pixel having adigital number provided by: receiving a first digital number for thepixel generated in response to radiance received by a first imagingsensor, wherein the first sensor is part of an imaging system comprisinga plurality of imaging sensors; determining, using an image correctionsystem and the first digital number, whether to correct for a non-linearresponse of the first sensor to the amount of radiance received; andgenerating, using the image correction system, if it is determined tocorrect for the response of the first sensor, a final digital number forthe pixel based on the first digital number and a second digital number,wherein the second digital number is generated in response to radiancereceived by a second imaging sensor of the imaging system; wherein saiddetermining comprises: identifying a roll-off point for the firstsensor, wherein the first sensor has a substantially non-linear responseto the amount of radiance it receives after the roll-off point is met;and deciding to perform said generating step if the first digital numberhas a value greater than a digital number associated with the roll-offpoint.
 12. The image of claim 11, wherein the final digital number isbased on digital numbers generated by the plurality of imaging sensorsother than the first sensor.
 13. The image of claim 12, whereingenerating the final digital number further comprises: calculating aratio corresponding to an amount of compensation for the first digitalnumber; calculating an average of the digital numbers generated by theplurality of imaging sensors other than the first sensor; andcalculating the final digital number based on the ratio and the average.14. An image enhancement system comprising: a transmit/receive systemconfigured to receive a first digital number generated in response toradiance received by a first imaging sensor, wherein the first sensor ispart of an imaging system comprising a plurality of imaging sensors; andan image correction system in communication with the transmit/receivestation and configured to: identify a roll-off point for the firstsensor, wherein the first sensor has a substantially non-linear responseto the amount of radiance it receives after the roll-off point is met;determine whether to correct for a non-linear response of the firstsensor to the amount of radiance received; and generate, if it isdetermined to correct for the response of the first sensor, a finaldigital number based on the first digital number and a second digitalnumber, wherein the second digital number is generated in response toradiance received by a second imaging sensor of the imaging system,wherein it is determined to correct for a non-linear response of thefirst sensor to the amount of radiance received if the first digitalnumber has a value greater than a digital number associated with theroll-off point.
 15. The image enhancement system of claim 14, whereinthe final digital number is based digital numbers generated by theplurality of imaging sensors other than the first sensor.
 16. The imageenhancement system of claim 15, wherein generating the final digitalnumber further comprises: calculating a ratio corresponding to an amountof compensation for the first digital number; calculating an average ofthe digital numbers generated by the plurality of imaging sensors otherthan the first sensor; and calculating the final digital number based onthe ratio and the average.